Global positioning systems may be used for tracking the location of humans. However, in certain environments, GPS signals may be unavailable for tracking human location. Where knowing the location of humans is important, such as in industrial safety, mining, underground maintenance, first responders, soldiers, and even shoppers within a mall and GPS is unavailable, personal navigation devices that provide user location have been developed. Many of these devices function through implementing the dead reckoning technique, where dead reckoning involves projecting a displacement onto a heading that is provided by a compass or gyro. To measure displacement, personal navigation systems may use human motion characteristics. For example, some systems may measure displacement by detecting and counting steps, such as pedometers, while other systems may differentiate between locomotion modes (such as walking, running, crawling, and the like) and then determine the step length based on a motion model that relates one or more measures of activity to displacement. These above described system rely on accurate methods of counting limb strikes (such as steps), and motion model based systems, in particular, rely on the precise measurement of a time of limb strike (such as a footfall) to be used as an input to the motion model. However, in some systems, misidentified steps may significantly contribute to measurement errors, thus, precise measurement of the number and time of limb strikes is important for high precision displacement calculations.
In certain personal navigation systems, pattern matching is used to discriminate between various motion types. Essentially, a measured inertial sensor signal pattern is matched to one of many pre-recorded patterns, where the pre-recorded patterns are associated with different motion types. However, for the different motion types, many different patterns need to be recorded to accommodate different sensor mounting locations and different human postures. Also, if start points of the measured and recorded patterns do not coincide, lengthy and computation intensive correlation processes are used to synchronize the patterns.